Journal of Educational Evaluation for Health Professions

Younyoung Choi

2 Articles

Estimation of item parameters and examinees’ mastery probability in each domain of the Korean Medical Licensing Examination using a deterministic inputs, noisy “and” gate (DINA) model: Younyoung Choi, Dong Gi Seo; J Educ Eval Health Prof. 2020;17:35. Published online November 17, 2020; DOI: https://doi.org/10.3352/jeehp.2020.17.35

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Purpose
The deterministic inputs, noisy “and” gate (DINA) model is a promising statistical method for providing useful diagnostic information about students’ level of achievement, as educators often want to receive diagnostic information on how examinees did on each content strand, which is referred to as a diagnostic profile. The purpose of this paper was to classify examinees of the Korean Medical Licensing Examination (KMLE) in different content domains using the DINA model.
Methods
This paper analyzed data from the KMLE, with 360 items and 3,259 examinees. An application study was conducted to estimate examinees’ parameters and item characteristics. The guessing and slipping parameters of each item were estimated, and statistical analysis was conducted using the DINA model.
Results
The output table shows examples of some items that can be used to check item quality. The probabilities of mastery of each content domain were also estimated, indicating the mastery profile of each examinee. The classification accuracy and consistency for 8 content domains ranged from 0.849 to 0.972 and from 0.839 to 0.994, respectively. As a result, the classification reliability of the cognitive diagnosis model was very high for the 8 content domains of the KMLE.
Conclusion
This mastery profile can provide useful diagnostic information for each examinee in terms of each content domain of the KMLE. Individual mastery profiles allow educators and examinees to understand which domain(s) should be improved in order to master all domains in the KMLE. In addition, all items showed reasonable results in terms of item parameters.

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Large-Scale Parallel Cognitive Diagnostic Test Assembly Using A Dual-Stage Differential Evolution-Based Approach
Xi Cao, Ying Lin, Dong Liu, Henry Been-Lirn Duh, Jun Zhang
IEEE Transactions on Artificial Intelligence.2024; 5(6): 3120. CrossRef

Usefulness of the DETECT program for assessing the internal structure of dimensionality in simulated data and results of the Korean nursing licensing examination: Dong Gi Seo, Younyoung Choi, Sun Huh; J Educ Eval Health Prof. 2017;14:32. Published online December 27, 2017; DOI: https://doi.org/10.3352/jeehp.2017.14.32

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Abstract PDF Supplementary Material

Purpose
The dimensionality of examinations provides empirical evidence of the internal test structure underlying the responses to a set of items. In turn, the internal structure is an important piece of evidence of the validity of an examination. Thus, the aim of this study was to investigate the performance of the DETECT program and to use it to examine the internal structure of the Korean nursing licensing examination.
Methods
Non-parametric methods of dimensional testing, such as the DETECT program, have been proposed as ways of overcoming the limitations of traditional parametric methods. A non-parametric method (the DETECT program) was investigated using simulation data under several conditions and applied to the Korean nursing licensing examination.
Results
The DETECT program performed well in terms of determining the number of underlying dimensions under several different conditions in the simulated data. Further, the DETECT program correctly revealed the internal structure of the Korean nursing licensing examination, meaning that it detected the proper number of dimensions and appropriately clustered the items within each dimension.
Conclusion
The DETECT program performed well in detecting the number of dimensions and in assigning items for each dimension. This result implies that the DETECT method can be useful for examining the internal structure of assessments, such as licensing examinations, that possess relatively many domains and content areas.

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Dubravka Svetina Valdivia, Debby Herbenick, Tsung-chieh Fu, Heather Eastman-Mueller, Lucia Guerra-Reyes, Molly Rosenberg
Journal of Sex & Marital Therapy.2022; 48(6): 579. CrossRef
The accuracy and consistency of mastery for each content domain using the Rasch and deterministic inputs, noisy “and” gate diagnostic classification models: a simulation study and a real-world analysis using data from the Korean Medical Licensing Examinat
Dong Gi Seo, Jae Kum Kim
Journal of Educational Evaluation for Health Professions.2021; 18: 15. CrossRef
Estimation of item parameters and examinees’ mastery probability in each domain of the Korean Medical Licensing Examination using a deterministic inputs, noisy “and” gate (DINA) model
Younyoung Choi, Dong Gi Seo
Journal of Educational Evaluation for Health Professions.2020; 17: 35. CrossRef
Linear programming method to construct equated item sets for the implementation of periodical computer-based testing for the Korean Medical Licensing Examination
Dong Gi Seo, Myeong Gi Kim, Na Hui Kim, Hye Sook Shin, Hyun Jung Kim
Journal of Educational Evaluation for Health Professions.2018; 15: 26. CrossRef

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